Abstract

AI-enabled monitoring in workplaces is expanding rapidly, promising gains in productivity, performance, and organisational oversight. However, it also raises critical concerns about employee privacy, autonomy, and power relations. While prior research has highlighted managerial and operational benefits of monitoring, limited attention has been paid to privacy risks and responsible AI practices. This research-in-progress addresses the question: What are the specific privacy challenges of AI-enabled monitoring in digital workplaces of the future? Drawing on Solove’s privacy taxonomy, Nissenbaum’s contextual integrity, and Foucault’s theory of power, we propose a conceptual model that traces data flows, processing, and dissemination in monitoring systems. Using a mixed-methods design, including qualitative interviews and survey validation, we aim to identify privacy risks and test propositions about consent, transparency, data dissemination, and power asymmetries. The study contributes to responsible AI discourse by offering theoretical insights and practical guidance for safeguarding employee privacy in AI-mediated workplaces.

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